I like to show the value_counts(normalize=True) of a series what works well, but I also wanna show the value_counts() not normalized in an additional column.
Code
import pandas as pd
cars = {'Brand': ['Honda Civic','Toyota Corolla','','Audi A4'],
'Price': [32000,35000,37000,45000]
}
df = pd.DataFrame(cars, columns = ['Brand', 'Price'])
df.Brand.value_counts(normalize=True)
Expected output
perc count
Toyota Corolla 0.25 1
Audi A4 0.25 1
Honda Civic 0.25 1
0.25 1
Name: Brand, dtype: float64
Question
How could I attache both information to the series?
If want use value_counts you need run code without normalize=True:
df = pd.concat([df.Brand.value_counts(normalize=True),
df.Brand.value_counts()],
axis=1,
keys=('perc','count'))
print (df)
perc count
0.25 1
Honda Civic 0.25 1
Toyota Corolla 0.25 1
Audi A4 0.25 1
Another idea is create perc column in another step, DataFrame.insert is for set position of new column:
df = df.Brand.value_counts().to_frame('count')
df.insert(0, 'perc', df['count'].div(len(df)))
print (df)
perc count
0.25 1
Honda Civic 0.25 1
Toyota Corolla 0.25 1
Audi A4 0.25 1
df = df.Brand.value_counts(normalize=True).to_frame('perc')
df['count'] = df['perc'].mul(len(df))
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